import { Container, Col, Row } from 'reactstrap'

import { LinkExternal } from 'src/components/Link/LinkExternal'

import youtubeTutorialThumbnail from 'src/assets/img/youtube-tutorial-thumbnail.jpg'

<Container>

**Q:** How do I use this tool? Is there a tutorial?

**A:** _Our tutorial video is available on YouTube:_

<Row>
  <Col className="d-flex">
    <LinkExternal className="mx-auto" href="https://www.youtube.com/watch?v=sv5T7MPKE5A" alt="Link to YouTube tutorial">
      <img className="img-fluid" src={youtubeTutorialThumbnail} alt="Link to YouTube tutorial thumbnail" />
    </LinkExternal>
  </Col>
</Row>

---

**Q:** How do I refer to the tool in publications? Is there a paper?

**A:** _We have posted a preprint describing covid19-scenarios.org on medRxiv. Please use this as a reference until our 
work appears in a journal. [doi: 10.1101/2020.05.05.20091363](https://doi.org/10.1101/2020.05.05.20091363 )_

---

**Q:** Why does the outbreak grow more slowly when I increase the infectious period?

**A:** _The number of secondary cases resulting from a particular case is specified by $R_0$. If you increase the infectious
period, the same number of infections occur, but over a longer time period. Hence the outbreak grows more slowly._

---

**Q:** Why is the number of severe cases lower than the number of critical cases?

**A:** _Critical condition COVID-19 cases require intensive care for a long time. In our model we assume that they spend an
average of 14 days in the ICU. Severely ill is our proxy for those in need of a regular hospital bed. These individuals will either deteriorate
rapidly or recover (default recovery time in our model is 4 days). Therefore, at any given point in time, the number of critically ill
cases can exceed the number of severely ill cases._

---

**Q:** Is the model fit to observations?

**A:** _Yes. Provided we have a good source of COVID-19 cases, we fit several model parameters to observations.
Specifically, we estimate $R_0$, the initial size and date of the epidemic, and the case underreporting fraction. For case
severity information we use an estimate from case outcome data from China._ 

Note: Currently mitigation efforts, both the timing and the efficacy, are _not_ estimated from the data. 
We are actively looking for user-provided dates of mitigation efforts for your region(s) of interest.

---

**Q:** My town/region/country is missing!

**A:** _If you have suggestions on additional regions that should be covered, head over to our 
[GitHub data directory](https://github.com/neherlab/covid19_scenarios/tree/master/data) and make a pull request!_

---

**Q:** What is "ICU overflow"?

**A:** _In places that have seen a severe COVID-19 outbreak, the capacity of intensive care facilities is quickly
exhausted. Due to the resulting resource shortage, patients that need ventilation cannot get access. "ICU overflow" is
our label for critically ill patients that should be ventilated, but are NOT, since no ventilators are available. These
patients will die faster; the degree to which is specified by the `Severity of ICU overflow` parameter._

---

**Q:** Wouldn't it be a good idea to model the isolation of specific age-groups?

**A:** _Yes! This is indeed possible on [covid-scenarios](https://covid19-scenarios.org/). Expand the card
`Age-Group-Specific Parameters`. The last column allows you to specify to what extent individual age
groups are isolated from the rest of the population._

---

**Q:** How does your model compare to the one by Imperial College London?

**A:** _You are probably referring to the
March 16 report by [Neil Ferguson et al](https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf).
Like us, Ferguson et al. use a computational model to investigate the effect of interventions on the spread of COVID-19. Their 
model is individual based, meaning their program represents a large number of individuals among whom the virus is
spreading. Our model breaks the population into age-groups and different categories corresponding to those susceptible,
infected, dead, recovered, etc. While this may lose some realism, our model type allows for faster simulations and
exploration of various parameters._

---

**Q:** What do the parameter ranges correspond to?

**A:** _Parameter ranges allow the user to specify the distribution of possible values. We assume a uniform prior, that is,
any value within the range has an equal probability of being chosen. The model is then run with a Monte Carlo sampling from
all specified parameter ranges for user-specified number of samples. The median trajectory, as well as the 20th and 80th percentiles,
are displayed as the shaded uncertainty region._

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**Q:** Why do the curves sometimes have strange kinks?

**A:** _The curves show the median of a number simulations sampled from the parameter ranges. If the simulations
corresponding to different parameter combinations intersect, the curve representing the median can change,
resulting in a kink._

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**Q:** What is a good number of simulations to run?

**A:** _We chose 15 as the default number of simulations to run. This is a good balance between sufficient sampling and maintaining interactivity.
Once you have discovered a set of parameters that you find reasonable, we suggest increasing the number to more accurately capture
the statistics._

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**Q:** Can I run a simulation with no uncertainty?

**A:** _If you set the lower and upper bounds of each parameter to the same value, your result will include only get one curve
and no uncertainty bands. Note that in this case the number of simulation runs is ignored._

---

**Q:** How do I interpret the figures reported under "Proportions"? Is a fatal case also counted as critical or severe case? Is a critical case also counted as severe case?

**A:** _These numbers sum to 100%. The number reported as "critical" is the fraction of infected people that fall critically ill but did not die.
Similarly, the reported fraction of "severe" cases is the proportion of cases that were severely ill but did not need critical care.
In essence, these numbers report the most serious outcome for each case._


</Container>
